According to foreign media reports, due to the high energy output and fast charging speed, capacitors will play an important role in powering future machines such as electric cars and mobile phones, but a major obstacle for capacitors to become energy storage devices is that their stored energy is far Far lower than batteries of the same size. Researchers at the Georgia Institute of Technology in the United States have found a novel way to solve the above problems. The researchers used supercomputers and machine learning techniques to eventually create more powerful capacitors. The research included teaching the computer to analyze the atoms of the two materials that make capacitors—aluminum and polyethylene.
(Source: University of California, San Diego)
Researchers are focused on finding ways to analyze the electronic structure of capacitor materials faster and find characteristics that can affect the performance of capacitors. Rampi Ramprasad, a professor at Georgia Tech's School of Materials Science and Engineering, said: "The electronics industry wants to understand the structure and properties of all materials used to produce electronic devices, including capacitors. For example, polyethylene It is a very good insulator, with a large band gap, and the energy range is beyond the reach of charge carriers, but it has a disadvantage that excess charge carriers can enter the band gap, thereby reducing efficiency. "
Ramprasad said: "In order to understand where the defects are and what role they play, we need to calculate the entire atomic structure of the material, which has been very difficult so far. The use of quantum mechanics to analyze such materials is too slow, which limits the analysis carried out in a specific time the amount."
Ramprasad and his colleagues used machine learning methods to develop new materials. They used quantum mechanics to analyze data samples produced by aluminum and polyethylene, and taught a powerful computer how to simulate such analysis. Analyzing the electronic structure of materials with quantum mechanics involves solving the Cohen-Sham equation of density functional theory, which generates wave function and energy level data, which can be used to calculate the total potential energy and atomic force of the system.
The researchers used the Comet supercomputer at the San Diego Supercomputer Center, an organized research unit at the University of California, San Diego, used for early computing; and also used the University of Texas at Austin, Texas Advanced Computing The Stampede2 supercomputer at the center was used in the later stages of this study.
Compared with the use of traditional techniques based on quantum mechanics, the similar results produced by the new machine learning method are orders of magnitude more. Ramprasad said: "The increase in computing power will allow us to design better electronic materials than existing materials."
Although this research focuses on aluminum and polyethylene, machine learning methods can be used to analyze the electronic structure of a wider variety of materials. Ramprasad said that in addition to being able to analyze electronic structures, other aspects of the material structure now analyzed by quantum mechanics can also be accelerated by machine learning methods.
Machine learning methods make processing faster, allowing researchers to more quickly simulate how changes in materials affect their electronic structure, and thus find new ways to increase their efficiency. Kamal said: "The supercomputer system capable of high throughput computing, which can create massive knowledge database for various material systems, and such knowledge can help us find the best material for specific applications." (Author: Yuqiu Yun)
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